CN116186017A - Big data collaborative supervision method and platform - Google Patents
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- CN116186017A CN116186017A CN202310450512.7A CN202310450512A CN116186017A CN 116186017 A CN116186017 A CN 116186017A CN 202310450512 A CN202310450512 A CN 202310450512A CN 116186017 A CN116186017 A CN 116186017A
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Abstract
The invention relates to the technical field of big data management, and particularly discloses a big data collaborative supervision method and a platform. The invention can realize multi-dimensional big data collaborative supervision based on longitudinal comparison, transverse comparison and association relation comparison, and improve the supervision efficiency and the supervision quality of a target supervision project.
Description
Technical Field
The invention belongs to the technical field of big data management, and particularly relates to a big data collaborative supervision method and a platform.
Background
Big data refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which needs a new processing mode to have stronger decision-making ability, insight discovery ability and flow optimization ability. With the rapid development of the internet industry, the business data volume of each industry is exponentially increased, and how to utilize a big data analysis means to realize the data supervision of corresponding business/projects so as to ensure the normal operation of the business is becoming a problem to be solved urgently for the big data management of each industry.
The existing big data supervision scheme generally only collects single-dimension supervision data of corresponding services/projects to carry out simple threshold comparison, so that whether the data are abnormal or not is judged according to a threshold comparison result, the mode does not fully consider the relevance between supervision service/project data and other relevant dimension data, collaborative supervision based on diversified data cannot be realized, and the achieved supervision quality and effect are still to be improved.
Disclosure of Invention
The invention aims to provide a big data collaborative supervision method and a platform, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, a big data collaborative supervision method is provided, including:
acquiring a data collaborative supervisory task instruction set for a target supervisory project, wherein the data collaborative supervisory task instruction set comprises project identification information, project supervisory rules and supervisory data reference intervals corresponding to the target supervisory project;
acquiring current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of a target supervision project from a distributed system according to project identification information of the target supervision project, wherein the distributed system is formed by connecting a plurality of scattered computers through an internet, and the distributed system stores the current monitoring data, the longitudinal collaborative monitoring data set, the transverse collaborative monitoring data set and the associated collaborative monitoring data set which are associated with each item of target identification information;
determining a longitudinal data comparison result of the target supervision item according to the current monitoring data and the longitudinal collaborative monitoring data set, determining a transverse data comparison result of the target supervision item according to the current monitoring data and the transverse collaborative monitoring data set, and determining a correlation data comparison result of the target supervision item according to the current monitoring data and the correlation collaborative monitoring data set;
calculating a longitudinal variation parameter according to the longitudinal data comparison result, calculating a transverse variation parameter according to the transverse data comparison result, and calculating a correlation variation parameter according to the correlation data comparison result;
determining a collaborative supervision adjustment coefficient according to the longitudinal change parameter, the transverse change parameter, the associated change parameter and the project supervision rule;
calculating monitoring adjustment data according to the current monitoring data of the collaborative supervision adjustment coefficient and the target supervision project;
and comparing the monitoring regulation data with the supervision data reference section, judging whether the target supervision project needs to be subjected to abnormal prompt according to the comparison result, and sending corresponding collaborative supervision prompt information according to the project identification information when judging that the target supervision project needs to be subjected to abnormal prompt.
In one possible design, the longitudinal collaborative monitoring dataset includes historical monitoring data for each historical monitoring time point of the target monitoring item in the distributed system, the lateral collaborative monitoring dataset includes similar monitoring data for each similar monitoring item of the target monitoring item at a current monitoring time point in the distributed system, and the associated collaborative monitoring dataset includes associated monitoring data for each associated monitoring item of the target monitoring item at the current monitoring time point in the distributed system.
In one possible design, the determining the longitudinal data comparison result of the target supervision item according to the current monitoring data and the longitudinal collaborative monitoring data set includes: and determining the difference value of each historical monitoring data in the current monitoring data and the longitudinal collaborative monitoring data set, forming a first difference value group by utilizing each difference value, and taking the first difference value group as a longitudinal data comparison result of the target supervision project.
In one possible design, the determining the lateral data comparison result of the target supervision item according to the current monitoring data and the lateral collaborative monitoring data set includes: and determining the difference value of each same kind of monitoring data in the current monitoring data and the transverse collaborative monitoring data set, forming a second difference value group by utilizing each difference value, and taking the second difference value group as a transverse data comparison result of the target supervision project.
In one possible design, the determining the associated data comparison result of the target supervision item according to the current monitoring data and the associated collaborative monitoring data set includes: determining the association type of the target supervision item and the corresponding association supervision item, and matching and calling corresponding association operation formulas from a database according to the association type, wherein a plurality of pre-configured association operation formulas are stored in the database, and each association operation formula is associated with the corresponding association type respectively; calculating corresponding operation values according to the associated operation formulas, the current monitoring data and the associated monitoring data of the corresponding associated supervision items; and determining the association values of the target supervision item and the corresponding association supervision item according to the operation values and the association types, forming an association value group by utilizing each association value, and taking the association value group as an association data comparison result of the target supervision item.
In one possible design, the calculating the longitudinal variation parameter according to the longitudinal data comparison result, the calculating the lateral variation parameter according to the lateral data comparison result, and the calculating the relevant variation parameter according to the relevant data comparison result includes: calculating a first standard deviation by using the longitudinal data comparison result, and taking the first standard deviation as a longitudinal variation parameter; calculating a second standard deviation by using the transverse data comparison result, and taking the second standard deviation as a transverse variation parameter; and calculating a third standard deviation by using the correlation data comparison result, and taking the third standard deviation as a correlation variation parameter.
In one possible design, the project supervision rule includes a set parameter calculation model, and the determining the collaborative supervision adjustment coefficient according to the longitudinal variation parameter, the lateral variation parameter, the associated variation parameter and the project supervision rule includes: and calling a parameter calculation model in the project supervision rule, substituting the longitudinal variation parameter, the transverse variation parameter and the related variation parameter into the parameter calculation model for calculation, and obtaining a collaborative supervision adjustment coefficient.
In one possible design, the monitoring adjustment data includes upper limit monitoring adjustment data and lower limit monitoring adjustment data, and the calculating the monitoring adjustment data according to the collaborative supervision adjustment coefficient and current monitoring data of the target supervision project includes: and respectively taking positive and negative of the collaborative supervision adjustment coefficient, adding one to the positive collaborative supervision adjustment coefficient and multiplying the positive collaborative supervision adjustment coefficient by the current monitoring data to obtain upper limit monitoring adjustment data, and adding one to the negative collaborative supervision adjustment coefficient and multiplying the negative collaborative supervision adjustment coefficient by the current monitoring data to obtain lower limit monitoring adjustment data.
In one possible design, the comparing the monitoring adjustment data with the reference interval of the supervision data, and determining whether the target supervision item needs to be abnormally prompted according to the comparison result includes: and comparing the upper limit monitoring adjustment data and the lower limit monitoring adjustment data with the supervision data reference interval respectively, judging that the target supervision project needs to be subjected to abnormal prompt if the upper limit monitoring adjustment data and the lower limit monitoring adjustment data are not in the supervision data reference interval, or judging that the target supervision project does not need to be subjected to abnormal prompt.
In a second aspect, a big data collaborative supervision platform is provided, including obtaining unit, collection unit, first determining unit, first calculating unit, second determining unit, second calculating unit, comparison unit and suggestion unit, wherein:
the acquisition unit is used for acquiring a data collaborative supervision task instruction set for a target supervision project, wherein the data collaborative supervision task instruction set comprises project identification information corresponding to the target supervision project, project supervision rules and a supervision data reference interval;
the acquisition unit is used for acquiring current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of the target supervision project from the distributed system according to project identification information of the target supervision project;
the first determining unit is used for determining a longitudinal data comparison result of the target supervision project according to the current monitoring data and the longitudinal collaborative monitoring data set, determining a transverse data comparison result of the target supervision project according to the current monitoring data and the transverse collaborative monitoring data set, and determining a correlation data comparison result of the target supervision project according to the current monitoring data and the correlation collaborative monitoring data set;
the first calculation unit is used for calculating a longitudinal variation parameter according to a longitudinal data comparison result, calculating a transverse variation parameter according to a transverse data comparison result and calculating a correlation variation parameter according to a correlation data comparison result;
the second determining unit is used for determining a collaborative supervision adjustment coefficient according to the longitudinal variable, the transverse variable, the related variable and the project supervision rule;
the second calculation unit is used for calculating monitoring adjustment data according to the collaborative supervision adjustment coefficient and the current monitoring data of the target supervision project;
the comparison unit is used for comparing the monitoring and adjusting data with the supervision data reference interval and judging whether the target supervision project needs to be subjected to abnormal prompt or not according to the comparison result;
and the prompting unit is used for sending out corresponding collaborative supervision prompting information according to the item identification information when the target supervision item is judged to need to be abnormally prompted.
In a third aspect, there is provided a computer device comprising:
a memory for storing instructions;
and a processor for reading the instructions stored in the memory and executing the method according to any one of the above first aspects according to the instructions.
In a fourth aspect, there is provided a computer readable storage medium having instructions stored thereon which, when run on a computer, cause the computer to perform the method of any of the first aspects. Also provided is a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects.
The beneficial effects are that: according to the invention, current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of a target supervision project are acquired from each data source of a distributed system through corresponding collaborative supervision task instructions to carry out analysis and comparison, a longitudinal data comparison result, a transverse data comparison result and an associated data comparison result are determined, then a collaborative supervision adjustment coefficient is obtained by calculating a set project supervision rule based on each comparison result, monitoring adjustment data of the target supervision project is calculated by utilizing the collaborative supervision adjustment coefficient, whether the target supervision project needs to be subjected to abnormal prompt is judged according to the monitoring adjustment data, and corresponding collaborative supervision prompt information is sent when the abnormal prompt is needed, so that distributed data collaborative supervision of the target supervision project is realized, and the comprehensiveness and accuracy of supervision on the target supervision project are improved. The invention can realize multi-dimensional big data collaborative supervision based on longitudinal comparison, transverse comparison and association relation comparison, and improve the supervision efficiency and the supervision quality of the target supervision project.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram showing the steps of the method of example 1 of the present invention;
FIG. 2 is a schematic view showing the construction of a platform according to embodiment 2 of the present invention;
fig. 3 is a schematic diagram of the configuration of a computer device in embodiment 3 of the present invention.
Detailed Description
It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention. Specific structural and functional details disclosed herein are merely representative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It should be appreciated that the terms first, second, etc. are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance. Although the terms first, second, etc. may be used herein to describe various features, these features should not be limited by these terms. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.
In the following description, specific details are provided to provide a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, the platform may be shown in a block diagram to avoid obscuring the examples with unnecessary detail. In other embodiments, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
Example 1:
the embodiment provides a big data collaborative supervision method, which can be applied to a corresponding big data collaborative supervision platform, as shown in fig. 1, and the method comprises the following steps:
s1, acquiring a data collaborative supervision task instruction set for a target supervision project, wherein the data collaborative supervision task instruction set comprises project identification information, project supervision rules and supervision data reference intervals corresponding to the target supervision project.
In specific implementation, the platform firstly acquires a data collaborative supervisory task instruction set for a target supervisory project, wherein the data collaborative supervisory task instruction set is an instruction set which is built in advance for the target supervisory project, and the data collaborative supervisory task instruction set comprises project identification information, project supervisory rules and supervisory data reference intervals corresponding to the target supervisory project.
S2, current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of the target supervision project are collected from the distributed system according to project identification information of the target supervision project.
When the method is implemented, after the data collaborative supervisory task instruction set is obtained, the item identification information contained in the instruction set can be utilized to collect current monitoring data, longitudinal collaborative monitoring data sets, transverse collaborative monitoring data sets and associated collaborative monitoring data sets of target supervisory items from all data source ends in the distributed system. The distributed system is an interactive collaboration system formed by connecting a plurality of data source ends, namely a plurality of scattered computers, through an internet, so that safe sharing of multi-source data can be realized, and each data source end, namely each computer in the distributed system, stores current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set which are associated with corresponding item identification information.
The item identification information may be a unique item number of the target supervision item. The longitudinal collaborative monitoring data set comprises historical monitoring data of each historical monitoring time point of a target supervision item in the distributed system, the transverse collaborative monitoring data set comprises similar monitoring data of each similar supervision item of the current monitoring time point of the target supervision item in the distributed system, and the associated collaborative monitoring data set comprises associated monitoring data of each associated supervision item of the current monitoring time point of the target supervision item in the distributed system.
S3, determining a longitudinal data comparison result of the target supervision project according to the current monitoring data and the longitudinal collaborative monitoring data set, determining a transverse data comparison result of the target supervision project according to the current monitoring data and the transverse collaborative monitoring data set, and determining a correlation data comparison result of the target supervision project according to the current monitoring data and the correlation collaborative monitoring data set.
In the implementation, after the current monitoring data, the longitudinal collaborative monitoring data set, the transverse collaborative monitoring data set and the related collaborative monitoring data set of the target supervision project are obtained: 1. and determining the difference value of each historical monitoring data in the current monitoring data and the longitudinal collaborative monitoring data set, forming a first difference value group by utilizing each difference value, and taking the first difference value group as a longitudinal data comparison result of the target supervision project. 2. And determining the difference value of each same kind of monitoring data in the current monitoring data and the transverse collaborative monitoring data set, forming a second difference value group by utilizing each difference value, and taking the second difference value group as a transverse data comparison result of the target supervision project. 3. Determining the association type of a target supervision item and a corresponding association supervision item, matching and calling corresponding association operation formulas from a database according to the association type, wherein a plurality of pre-configured association operation formulas are stored in the database, each association operation formula is associated with the corresponding association type respectively, if the association type is positive correlation, the pre-configured association operation formulas corresponding to the association type in the database are numerical comparison operation formulas S=A/B, S represents operation values, A represents current monitoring data, B represents association monitoring data of the corresponding association supervision item, and the like, and the association operation formulas in the database can be configured and updated according to actual conditions; then, corresponding operation values are obtained through calculation according to the associated operation formulas, the current monitoring data and the associated monitoring data of the corresponding associated supervision items; determining the association value of the target supervision item and the corresponding association supervision item according to the operation value and the association type, and importing the operation value into a corresponding association value interval table according to the association type to obtain a corresponding association value by matching; and finally, forming an association value group by using each association value, and taking the association value group as an association data comparison result of the target supervision project.
S4, calculating a longitudinal variation parameter according to the longitudinal data comparison result, calculating a transverse variation parameter according to the transverse data comparison result, and calculating a correlation variation parameter according to the correlation data comparison result.
When the method is implemented, after a longitudinal data comparison result, a transverse data comparison result and a related data comparison result are obtained, a first standard deviation can be calculated by utilizing the longitudinal data comparison result, and the first standard deviation is used as a longitudinal variation parameter; calculating a second standard deviation by using the transverse data comparison result, and taking the second standard deviation as a transverse variation parameter; and calculating a third standard deviation by using the correlation data comparison result, and taking the third standard deviation as a correlation variation parameter.
S5, determining a collaborative supervision adjustment coefficient according to the longitudinal variable, the transverse variable, the associated variable and the project supervision rule.
When the method is specifically implemented, the project supervision rule comprises a preset parameter calculation model, the parameter calculation model can be directly called according to the project supervision rule, and after longitudinal variation parameters, transverse variation parameters and associated variation parameters are obtained, the longitudinal variation parameters, the transverse variation parameters and the associated variation parameters can be substituted into the parameter calculation model for calculation, so that the collaborative supervision adjustment coefficient is obtained. The parameter calculation model can be specifically designed and constructed according to the actual project supervision requirement, the parameter calculation model is not limited herein, and the collaborative supervision adjustment coefficient is a coefficient in a 0-1 interval.
S6, calculating monitoring adjustment data according to the collaborative monitoring adjustment coefficient and the current monitoring data of the target monitoring project.
In specific implementation, after the corresponding collaborative supervision adjustment coefficient is obtained by calculation, the collaborative supervision adjustment coefficient can be respectively taken positive and negative, the positive collaborative supervision adjustment coefficient is added with one and then multiplied by the current monitoring data to obtain upper limit monitoring adjustment data, the negative collaborative supervision adjustment coefficient is added with one and then multiplied by the current monitoring data to obtain lower limit monitoring adjustment data, and the monitoring adjustment data comprises the upper limit monitoring adjustment data and the lower limit monitoring adjustment data.
S7, comparing the monitoring and adjusting data with the supervision data reference section, judging whether the target supervision project needs to be subjected to abnormal prompt according to the comparison result, and sending corresponding collaborative supervision prompt information according to the project identification information when judging that the target supervision project needs to be subjected to abnormal prompt.
When the method is implemented, after the upper limit monitoring adjustment data and the lower limit monitoring adjustment data are determined, the upper limit monitoring adjustment data and the lower limit monitoring adjustment data are used for comparing with the supervision data reference interval respectively, if the upper limit monitoring adjustment data and the lower limit monitoring adjustment data are not in the supervision data reference interval, the target supervision project is judged to need to be subjected to abnormal prompt, if one or both of the upper limit monitoring adjustment data and the lower limit monitoring adjustment data are in the supervision data reference interval, the target supervision project is judged to not need to be subjected to abnormal prompt, and the accuracy and the reliability of coordinated supervision are ensured through interval comparison of the upper limit and the lower limit. When the abnormal prompt is required to be carried out on the target supervision project, the platform can generate cooperative supervision prompt information of the target supervision project according to the project identification information, and then the cooperative supervision prompt information is sent to the corresponding supervision terminal so that the supervision personnel can carry out corresponding coping.
The method can realize multi-dimensional big data collaborative supervision based on longitudinal comparison, transverse comparison and association relation comparison, and improve supervision efficiency and supervision quality of a target supervision project.
Example 2:
the embodiment provides a big data collaborative supervision platform, as shown in fig. 2, including an acquisition unit, a first determination unit, a first calculation unit, a second determination unit, a second calculation unit, a comparison unit and a prompt unit, wherein:
the acquisition unit is used for acquiring a data collaborative supervision task instruction set for a target supervision project, wherein the data collaborative supervision task instruction set comprises project identification information corresponding to the target supervision project, project supervision rules and a supervision data reference interval;
the acquisition unit is used for acquiring current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of the target supervision project from the distributed system according to project identification information of the target supervision project;
the first determining unit is used for determining a longitudinal data comparison result of the target supervision project according to the current monitoring data and the longitudinal collaborative monitoring data set, determining a transverse data comparison result of the target supervision project according to the current monitoring data and the transverse collaborative monitoring data set, and determining a correlation data comparison result of the target supervision project according to the current monitoring data and the correlation collaborative monitoring data set;
the first calculation unit is used for calculating a longitudinal variation parameter according to a longitudinal data comparison result, calculating a transverse variation parameter according to a transverse data comparison result and calculating a correlation variation parameter according to a correlation data comparison result;
the second determining unit is used for determining a collaborative supervision adjustment coefficient according to the longitudinal variable, the transverse variable, the related variable and the project supervision rule;
the second calculation unit is used for adjusting and calculating the current monitoring data of the target supervision project by utilizing the collaborative supervision adjustment coefficient to obtain monitoring and adjusting data;
the comparison unit is used for comparing the monitoring and adjusting data with the supervision data reference interval and judging whether the target supervision project needs to be subjected to abnormal prompt or not according to the comparison result;
and the prompting unit is used for sending out corresponding collaborative supervision prompting information according to the item identification information when the target supervision item is judged to need to be abnormally prompted.
Example 3:
the present embodiment provides a computer device, as shown in fig. 3, at a hardware level, including:
the data interface is used for establishing data butt joint between the processor and the distributed system;
a memory for storing instructions;
and the processor is used for reading the instruction stored in the memory and executing the big data collaborative supervision method in the embodiment 1 according to the instruction.
The device also optionally includes an internal bus through which the processor and memory and data interface may be interconnected, which may be an ISA (Industry Standard Architecture ) bus, PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc.
The Memory may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), flash Memory (Flash Memory), first-in first-out Memory (First Input First Output, FIFO), and/or first-in last-out Memory (First In Last Out, FILO), etc. The processor may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Example 4:
the present embodiment provides a computer-readable storage medium having instructions stored thereon that, when executed on a computer, cause the computer to perform the big data collaborative supervision method in embodiment 1. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable platforms.
The present embodiment also provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the big data collaborative supervision method of embodiment 1. Wherein the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable platform.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The big data collaborative supervision method is characterized by comprising the following steps of:
acquiring a data collaborative supervisory task instruction set for a target supervisory project, wherein the data collaborative supervisory task instruction set comprises project identification information, project supervisory rules and supervisory data reference intervals corresponding to the target supervisory project;
acquiring current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of a target supervision project from a distributed system according to project identification information of the target supervision project, wherein the distributed system is formed by connecting a plurality of scattered computers through an internet, and the distributed system stores the current monitoring data, the longitudinal collaborative monitoring data set, the transverse collaborative monitoring data set and the associated collaborative monitoring data set which are associated with each item of target identification information;
determining a longitudinal data comparison result of the target supervision item according to the current monitoring data and the longitudinal collaborative monitoring data set, determining a transverse data comparison result of the target supervision item according to the current monitoring data and the transverse collaborative monitoring data set, and determining a correlation data comparison result of the target supervision item according to the current monitoring data and the correlation collaborative monitoring data set;
calculating a longitudinal variation parameter according to the longitudinal data comparison result, calculating a transverse variation parameter according to the transverse data comparison result, and calculating a correlation variation parameter according to the correlation data comparison result;
determining a collaborative supervision adjustment coefficient according to the longitudinal change parameter, the transverse change parameter, the associated change parameter and the project supervision rule;
calculating monitoring adjustment data according to the current monitoring data of the collaborative supervision adjustment coefficient and the target supervision project;
and comparing the monitoring regulation data with the supervision data reference section, judging whether the target supervision project needs to be subjected to abnormal prompt according to the comparison result, and sending corresponding collaborative supervision prompt information according to the project identification information when judging that the target supervision project needs to be subjected to abnormal prompt.
2. The big data collaborative monitoring method according to claim 1, wherein the longitudinal collaborative monitoring dataset comprises historical monitoring data of each historical monitoring time point of a target monitoring item in the distributed system, the transverse collaborative monitoring dataset comprises similar monitoring data of each similar monitoring item of the target monitoring item at a current monitoring time point in the distributed system, and the associated collaborative monitoring dataset comprises associated monitoring data of each associated monitoring item of the target monitoring item at the current monitoring time point in the distributed system.
3. The big data collaborative monitoring method according to claim 2, wherein determining a longitudinal data comparison result of a target monitored item based on current monitoring data and a longitudinal collaborative monitoring data set comprises: and determining the difference value of each historical monitoring data in the current monitoring data and the longitudinal collaborative monitoring data set, forming a first difference value group by utilizing each difference value, and taking the first difference value group as a longitudinal data comparison result of the target supervision project.
4. A big data collaborative supervision method according to claim 3, wherein the determining a lateral data comparison of a target supervision item based on current monitoring data and a lateral collaborative monitoring data set comprises: and determining the difference value of each same kind of monitoring data in the current monitoring data and the transverse collaborative monitoring data set, forming a second difference value group by utilizing each difference value, and taking the second difference value group as a transverse data comparison result of the target supervision project.
5. The big data collaborative monitoring method according to claim 4, wherein determining the associated data comparison result of the target monitoring item according to the current monitoring data and the associated collaborative monitoring data set comprises: determining the association type of the target supervision item and the corresponding association supervision item, and matching and calling corresponding association operation formulas from a database according to the association type, wherein a plurality of pre-configured association operation formulas are stored in the database, and each association operation formula is associated with the corresponding association type respectively; calculating corresponding operation values according to the associated operation formulas, the current monitoring data and the associated monitoring data of the corresponding associated supervision items; and determining the association values of the target supervision item and the corresponding association supervision item according to the operation values and the association types, forming an association value group by utilizing each association value, and taking the association value group as an association data comparison result of the target supervision item.
6. The big data collaborative supervision method according to claim 5, wherein the calculating the longitudinal variation parameter according to the longitudinal data comparison result, the calculating the lateral variation parameter according to the lateral data comparison result, and the calculating the association variation parameter according to the association data comparison result comprises: calculating a first standard deviation by using the longitudinal data comparison result, and taking the first standard deviation as a longitudinal variation parameter; calculating a second standard deviation by using the transverse data comparison result, and taking the second standard deviation as a transverse variation parameter; and calculating a third standard deviation by using the correlation data comparison result, and taking the third standard deviation as a correlation variation parameter.
7. The big data collaborative supervision method according to claim 1, wherein the project supervision rules comprise a set parameter calculation model, and the determining collaborative supervision adjustment coefficients according to the longitudinal change parameter, the lateral change parameter, the associated change parameter and the project supervision rules comprises: and calling a parameter calculation model in the project supervision rule, substituting the longitudinal variation parameter, the transverse variation parameter and the related variation parameter into the parameter calculation model for calculation, and obtaining a collaborative supervision adjustment coefficient.
8. The big data collaborative supervision method according to claim 7, wherein the monitoring adjustment data includes upper limit monitoring adjustment data and lower limit monitoring adjustment data, the calculating the monitoring adjustment data according to the collaborative supervision adjustment coefficient and current monitoring data of the target supervision project includes: and respectively taking positive and negative of the collaborative supervision adjustment coefficient, adding one to the positive collaborative supervision adjustment coefficient and multiplying the positive collaborative supervision adjustment coefficient by the current monitoring data to obtain upper limit monitoring adjustment data, and adding one to the negative collaborative supervision adjustment coefficient and multiplying the negative collaborative supervision adjustment coefficient by the current monitoring data to obtain lower limit monitoring adjustment data.
9. The big data collaborative supervision method according to claim 8, wherein the comparing the monitoring adjustment data with the supervision data reference interval, and determining whether the target supervision item needs to be abnormally prompted according to the comparison result, comprises: and comparing the upper limit monitoring adjustment data and the lower limit monitoring adjustment data with the supervision data reference interval respectively, judging that the target supervision project needs to be subjected to abnormal prompt if the upper limit monitoring adjustment data and the lower limit monitoring adjustment data are not in the supervision data reference interval, or judging that the target supervision project does not need to be subjected to abnormal prompt.
10. Big data cooperation supervision platform, its characterized in that includes acquisition unit, collection unit, first determining unit, first calculating unit, second determining unit, second calculating unit, comparison unit and suggestion unit, wherein:
the acquisition unit is used for acquiring a data collaborative supervision task instruction set for a target supervision project, wherein the data collaborative supervision task instruction set comprises project identification information corresponding to the target supervision project, project supervision rules and a supervision data reference interval;
the acquisition unit is used for acquiring current monitoring data, a longitudinal collaborative monitoring data set, a transverse collaborative monitoring data set and an associated collaborative monitoring data set of the target supervision project from the distributed system according to project identification information of the target supervision project;
the first determining unit is used for determining a longitudinal data comparison result of the target supervision project according to the current monitoring data and the longitudinal collaborative monitoring data set, determining a transverse data comparison result of the target supervision project according to the current monitoring data and the transverse collaborative monitoring data set, and determining a correlation data comparison result of the target supervision project according to the current monitoring data and the correlation collaborative monitoring data set;
the first calculation unit is used for calculating a longitudinal variation parameter according to a longitudinal data comparison result, calculating a transverse variation parameter according to a transverse data comparison result and calculating a correlation variation parameter according to a correlation data comparison result;
the second determining unit is used for determining a collaborative supervision adjustment coefficient according to the longitudinal variable, the transverse variable, the related variable and the project supervision rule;
the second calculation unit is used for calculating monitoring adjustment data according to the collaborative supervision adjustment coefficient and the current monitoring data of the target supervision project;
the comparison unit is used for comparing the monitoring and adjusting data with the supervision data reference interval and judging whether the target supervision project needs to be subjected to abnormal prompt or not according to the comparison result;
and the prompting unit is used for sending out corresponding collaborative supervision prompting information according to the item identification information when the target supervision item is judged to need to be abnormally prompted.
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